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Few contemporary organizations are able to conduct business unencumbered by legacy systems, siloed access to data, and a reliance on rearward-looking metrics. MIT Sloan Management Review Strategic Measurement guest editor Michael Schrage sat down with Andrew Low Ah Kee, chief revenue officer of GoDaddy, to discuss these challenges as well as the opportunities presented by data-driven decision-making. For the Silicon Valley upstart, an analytics-first culture is second nature, and early strategic alignment leads to a nimble, fast-growing enterprise capable of shifting from a retrospective focus to a predictive one. Low Ah Kee describes some of the company’s use cases for machine-learning algorithms as he shares his views on aligning an organization around key performance indicators (KPIs).

Defining the right KPIs for machine-learning efforts is a sophisticated, but not necessarily complex, endeavor. Low Ah Kee advises organizations to start with a well-articulated (preferably, written) strategy and a commitment to gather only as much detailed data as is necessary for decision-making. He does not recommend prioritizing gathering more data over taking action, and offers insights into being purposeful about the decisions one chooses to automate. Finally, he suggests meetings begin with a scorecard review to ensure the organization is continuing to track the right metrics.

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About the Authors

Michael Schrage is a research fellow at the MIT Sloan School’s Initiative on the Digital Economy, where he does research and advisory work on how digital media transforms agency, human capital, and innovation. Andrew Low Ah Kee is the chief revenue officer of GoDaddy, where he is responsible for GoDaddy’s go-to-market efforts around the world.